Data-driven machine learning approaches have recently been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing methods do not well handle the topology information that directly impacts the network optimization results. Directly operating on simple representations, e.g., adjacency matrices, results in poor generalization performance as the learned results depend on specific ordering of the network elements in the training data. To address this issue, we propose a two-stage topology-aware machine learning framework (TALF), which trains a graph embedding unit and a deep feed-forward network (DFN) jointly. By propagating and summarizing the underlying graph topological information, TALF encodes the topology in the vector representation of the optimization instance, which is used by the later DFN to infer critical structures of an optimal or near-optimal solution. The proposed approach is evaluated on a canonical wireless network flow problem with diverse network typologies and flow deployments. In-depth study on trade-off between efficiency and effectiveness of the inference results is also conducted, and we show that our approach is better at differentiate links by saving up to 60% computation time at over 90% solution quality.
翻译:最近提出了数据驱动的机器学习方法,以便通过从历史优化实例中学习潜在知识,促进无线网络优化;然而,现有方法并不很好地处理直接影响网络优化结果的地形信息;直接以简单的表达方式运作,例如相邻矩阵,由于学习的结果取决于培训数据中网络要素的具体顺序,造成一般性表现不佳;为解决这一问题,我们提议了一个两阶段的地形意识机器学习框架(TALF),用于联合培训一个图形嵌入单元和一个深厚的饲料前向网络(DFN),通过传播和总结基本图表表层信息,TALF将优化实例矢量代表中的地形学编码,后者用来推断最佳或接近最佳解决办法的关键结构。为解决这一问题,我们建议的方法是对具有不同网络类型和流动部署的无线网络流动问题进行评价。还进行了关于预测结果的效率和效益之间的权衡的深入研究。我们表明,我们的方法通过在90年的时间里将60%的计算结果保存到60 %,从而更好地区分质量解决方案的链接。